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LinkedIn interview notes, page 18 of 32

Large Enterprises

Most of these LinkedIn write-ups come from Software Engineers (156), then Product Managers (66) and Data Scientists (37). The most reported rounds are the technical phone screen (169), the product sense round (62), the system design round (42) and the behavioral / leadership round (20).

316 interview notes · updated Oct 2026

LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn system design round for a software engineer role, centered entirely on designing a calendar backend. Pretty deep dive, they pushed hard on recurrence and the notification pipeline specifically.
  • Design the backend for a calendar service supporting event creation, invitations, recurring events, reminders, and free/busy availability across users.
  • An organizer edits the time of one occurrence in a weekly recurring event that 200 people have already accepted. Walk through what gets written and what each attendee sees, including the 'this event only', 'this and following', and 'all events' options.
  • How would you implement an efficient free/busy and conflict check across 30 attendees without scanning each person's full event history?
  • A user in the America/New_York timezone sets up a recurring 9am standup. How do you store and expand the recurrence rule so it keeps firing at local 9am even across daylight saving transitions?
  • Reminder workers fall behind during a traffic spike. How do you prevent reminders from being dropped or firing hours late, and how do you avoid duplicates when a worker retries?

“This is the kind of question where you think you know where to start and then realize five minutes in that you've already painted yourself into a corner.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn Data Scientist interview that was basically one meaty coding problem about reservoir sampling. The kind of question where you either know the algorithm or you're fumbling through probability logic on the spot.
  • You have an unbounded stream of items that can't all fit in memory. Write Python code to maintain a uniform random sample of size k from the stream, where the total number of items is unknown in advance. Explain the algorithm, its time and space complexity, and any edge cases.

“This is reservoir sampling and I'd seen it before, which saved me.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn system design round for a software engineer role, one big open-ended question about building a metrics monitoring platform from scratch. Felt like a lot of ground to cover in one session, but the structure helped keep things moving.
  • Design a metrics monitoring system that ingests time-series data from a large fleet of services and supports real-time querying, alerting, and dashboards. Walk through functional requirements, non-functional requirements, architecture, storage strategy, alert evaluation, and scaling trade-offs.

“This one is wide.”

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LinkedIn·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn ML Engineer interview focused almost entirely on a single system design problem around course recommendations. The whole thing lived or died on how well you could explain feature extraction from job postings, which I didn't fully appreciate until I was already in the middle of it.
  • Design a recommendation system that suggests LinkedIn Learning courses to users based on the jobs they have recently applied to or clicked on.

“I started with candidate generation and ranking, which felt natural, but the interviewer kept pulling me back to feature extraction from job postings.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview that went deep into dynamic programming, specifically the house painting / color assignment problem. They wanted both a top-down memoized solution and a space-optimized bottom-up version, plus path reconstruction. More involved than I expected for a single question.
  • Given H items arranged in a line and an H×C cost matrix where cost[i][c] is the cost of assigning color c to item i, design a solution that finds the minimum total cost to color all items such that no two adjacent items share the same color. Implement both a top-down recursive approach with memoization and a space-optimized bottom-up DP. Also reconstruct one valid optimal coloring.

“This is basically the paint house problem but generalized to C colors.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview with a binary search problem that looks straightforward until you actually think about duplicates. One question, pretty focused session.
  • Given a sorted integer array that has been rotated at an unknown pivot and may contain duplicate values, write a function that returns true if a target value exists in the array and false otherwise.

“The duplicates part is what gets you.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

May 2026
LinkedIn ML engineer interview with a classic probability/coding puzzle. Nothing too wild but it made me think harder than I expected.
  • You're given a function that flips a biased coin (unknown probability of heads). Write a new function that uses it to produce heads or tails with exactly 50/50 probability.

“I knew the Von Neumann trick from somewhere in the back of my head but couldn't remember the name, so I just re-derived it.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
LinkedIn PM interview, product sense round, single question about building a reactions feature. Pretty open-ended and I kept second-guessing whether they wanted a full product spec or just a high-level walkthrough.
  • You're a PM at LinkedIn tasked with building a reactions feature. How would you approach this?

“I started by questioning whether reactions even made sense for LinkedIn's context, which I think was the right instinct but I spent too long on it.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview that came down to implementing an LFU cache from scratch. Pretty classic hard problem but the O(1) constraint is what makes it actually interesting.
  • Design and implement an LFU cache that supports get and put operations in O(1) average time, with eviction of the least frequently used key and LRU tiebreaking.

“The naive solution comes to mind fast, a simple frequency map, but then they ask about O(1) and you realize a sorted structure won't cut it.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE round focused on LeetCode 381 (Insert Delete GetRandom with duplicates), but the real curveball was that the interviewer barely cared about the implementation itself and spent most of the time asking me to verbally walk through a testing strategy. Not what I prepped for.
  • Design a data structure that supports insert, remove, and getRandom in average O(1) time, where duplicate values are allowed.
  • Walk me through how you would test this data structure, including edge cases like an empty structure, a single element, removing a duplicate versus a non-existent element, and verifying getRandom distribution.
  • How would you test the behavior of the data structure under repeated insert and remove cycles, and what would you be looking for?

“I knew this problem and had the hashmap-plus-array approach ready.”

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